The capability of detecting an unpaved road in arid environments can greatly enhance an explosive hazard detection
system. One approach is to segment out the off-road area and the area above the horizon, which is considered to be
irrelevant for the task in hand. Segmenting out irrelevant areas, such as the region above the horizon, allows the
explosive hazard detection system to process a smaller region in a scene, enabling a more computationally complex
approach. In this paper, we propose a novel approach for speeding up the detection algorithms based on random
projection and random selection. Both methods have a low computational cost and reduce the dimensionality of the data
while approximately preserving, with a certain probability, the pair-wise point distances. Dimensionality reduction
allows any classifier employed in our proposed algorithm to consume fewer computational resources. Furthermore, by
applying the random projections directly to image intensity patches, there is no feature extraction needed. The data used
in our proposed algorithms are obtained from sensors on board a U.S. Army countermine vehicle. We tested our
proposed algorithms on data obtained from several runs on an arid climate road. In our experiments we compare our
algorithms based on random projection and random selection to Principal Component Analysis (PCA), a popular
dimensionality reduction method.